This invention relates generally to memory circuits, and more particularly to neuromorphic memory systems for neuromorphic applications.
The term “neuron” was coined by Heinrich Wilhelm Gottfried von Waldeyer-Hartz in 1891 to capture the discrete information processing units of the brain. The junctions between two neurons were termed “synapses” by Sir Charles Sherrington in 1897. Information flows only along one direction through a synapse, thus we talk about a “presynaptic” and a “postsynaptic” neuron. Neurons, when activated by sufficient input received via synapses, emit “spikes” that are delivered to those synapses that the neuron is pre-synaptic to.
It is theorized that experiences are captured as conductance of the synapses in the brain. The synaptic conductance changes with time as a function of the relative spike times of presynaptic and post-synaptic neurons. The conductance of a synapse increases if a postsynaptic neuron fires after its pre-synaptic neuron fires, and decreases in conductance if the order of the two firings is reversed. Furthermore, the change depends on the precise delay between the two events, such that the more the delay, the less the magnitude of change.
Neuromorphic systems, also referred to as artificial neural networks, are computational systems that permit electronic systems to essentially function in a manner analogous to that of biological brains. Neuromorphic systems do not generally utilize a traditional digital model of manipulating 0s and 1s. Instead, neuromorphic systems create connections between processing elements that are roughly functionally equivalent to neurons of a biological brain. Neuromorphic systems may include various electronic circuits that model biological neurons.
One postsynaptic neuron can be connected to multiple presynaptic neurons. In a circuit with thousands of neurons, connecting postsynaptic neurons to presynaptic neurons can become very complicated.